Neolithic and medieval virus genomes reveal complex ...

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Received: 14 March 2018

Accepted: 09 May 2018

Published: 10 May 2018

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Neolithic and medieval virus genomesreveal complex evolution of hepatitis BBen Krause-Kyora1,2†*, Julian Susat1†, Felix M Key2, Denise Kuhnert2,3,Esther Bosse1,4, Alexander Immel1,2, Christoph Rinne5, Sabin-Christin Kornell1,Diego Yepes4, Soren Franzenburg1, Henrike O Heyne6,7,8, Thomas Meier9,10,Sandra Losch11, Harald Meller12, Susanne Friederich12, Nicole Nicklisch12,13,Kurt W Alt12,13,14,15, Stefan Schreiber1,16, Andreas Tholey4, Alexander Herbig2,Almut Nebel1, Johannes Krause2*

1Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany; 2Max PlanckInstitute for the Science of Human History, Jena, Germany; 3Division of InfectiousDiseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland;4Systematic Proteomics & Bioanalytics, Institute for Experimental Medicine, KielUniversity, Kiel, Germany; 5Institute of Pre- and Protohistoric Archaeology, KielUniversity, Kiel, Germany; 6Stanley Center for Psychiatric Research, Broad Institute,Cambridge, United States; 7Analytic and Translational Genetics Unit, MassachusettsGeneral Hospital, Boston, United States; 8Program in Medical and PopulationGenetics, Broad Institute of MIT & Harvard, Cambridge, United States; 9Institute forPre- and Protohistory and Near Eastern Archaeology, Heidelberg University,Heidelberg, Germany; 10Heidelberg Center for the Environment, HeidelbergUniversity, Heidelberg, Germany; 11Department of Physical Anthropology, Instituteof Forensic Medicine, University of Bern, Bern, Switzerland; 12State Office forHeritage Management and Archaeology Saxony-Anhalt, State Museum ofPrehistory, Halle, Germany; 13Danube Private University, Krems, Austria;14Department of Biomedical Engineering, University Hospital Basel, University ofBasel, Basel, Switzerland; 15Integrative Prehistory and Archaeological Science,University of Basel, Basel, Switzerland; 16Clinic for Internal Medicine, UniversityHospital Schleswig-Holstein, Kiel, Germany

Abstract The hepatitis B virus (HBV) is one of the most widespread human pathogens known

today, yet its origin and evolutionary history are still unclear and controversial. Here, we report the

analysis of three ancient HBV genomes recovered from human skeletons found at three different

archaeological sites in Germany. We reconstructed two Neolithic and one medieval HBV genome

by de novo assembly from shotgun DNA sequencing data. Additionally, we observed HBV-specific

peptides using paleo-proteomics. Our results demonstrated that HBV has circulated in the

European population for at least 7000 years. The Neolithic HBV genomes show a high genomic

similarity to each other. In a phylogenetic network, they do not group with any human-associated

HBV genome and are most closely related to those infecting African non-human primates. The

ancient viruses appear to represent distinct lineages that have no close relatives today and possibly

went extinct. Our results reveal the great potential of ancient DNA from human skeletons in order

to study the long-time evolution of blood borne viruses.

DOI: https://doi.org/10.7554/eLife.36666.001

Krause-Kyora et al. eLife 2018;7:e36666. DOI: https://doi.org/10.7554/eLife.36666 1 of 15

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IntroductionThe hepatitis B virus (HBV) is one of the most widespread human pathogens, with worldwide over

250 million people being infected, and an annual death toll of about 1 million globally

(WHO, 2017). Infection of liver cells with HBV leads to acute hepatitis B, which is self-limiting in

about 90–95% of cases. In about 5–10% of infected individuals virus clearance fails and patients

develop chronic infection of hepatitis B, which puts them at lifelong elevated risk for liver cirrhosis

and liver cancer (hepatocellular carcinoma). HBV is usually transmitted by contact with infectious

blood, in highly endemic countries often during birth (WHO, 2017).

HBV has a circular, partially double-stranded DNA genome of about 3.2kbp that encodes four

overlapping open reading frames (P, pre-S/S, pre-C/C, and X). Based on the genomic sequence

diversity, HBVs are currently classified into eight genotypes (A-H) and numerous subgenotypes that

show distinct geographic distributions (Castelhano et al., 2017). All genotypes are hypothesised to

be primarily the result of recombination events (Littlejohn et al., 2016; Simmonds and Midgley,

2005). To a lesser extent, HBV evolution is also driven by the accumulation of point mutations

(Schaefer, 2007; Araujo, 2015).

Despite being widespread and well-studied, the origin and evolutionary history of HBV are still

unclear and controversial (Littlejohn et al., 2016; Souza et al., 2014). HBVs in non-human primates

(NHP), for instance in chimpanzees and gorillas, are phylogenetically closely related to, and yet dis-

tinct from, human HBV isolates, supporting the notion of an Africa origin of the virus (Souza et al.,

2014). Molecular-clock-based analyses dating the origin of HBV have resulted in conflicting esti-

mates with some as recent as about 400 years ago (Zhou and Holmes, 2007; Souza et al., 2014).

These observations have raised doubts about the suitability of molecular dating approaches for

reconstructing the evolution of HBV (Bouckaert et al., 2013 , Souza et al., 2014). Moreover, ancient

DNA (aDNA) research on HBV-infected mummies from the 16th century AD revealed a very close

relationship between the ancient and modern HBV genomes (Kahila Bar-Gal et al., 2012;

Patterson Ross et al., 2018), indicating a surprising lack of temporal genetic changes in the virus

during the last 500 years (Patterson Ross et al., 2018). Therefore, diachronic aDNA HBV

studies are necessary, in which both the changes in the viral genome over time as well as the prove-

nance and age of the archaeological samples are investigated, to better understand the origin and

evolutionary history of the virus.

Here, we report the analysis of three complete HBV genomes recovered from human skeletal

remains from the prehistoric Neolithic and Medieval Periods in Central Europe. Our results show

that HBV already circulated in the European population more than 7000 years ago. Although the

ancient forms show a relationship to modern isolates they appear to represent distinct lineages that

have no close modern relatives and are possibly extinct today.

Results and discussionWe detected evidence for presence of ancient HBV in three human tooth samples as part of a meta-

genomic screening for viral pathogens that was performed on shotgun sequencing data from 53

skeletons using the metagenomic alignment software MALT (Vagene et al., 2018). The remains of

the individuals were excavated from the Neolithic sites of Karsdorf (Linearbandkeramik [LBK], 5056–

4959 cal BC) and Sorsum (Tiefstichkeramik group of the Funnel Beaker culture, 3335–3107 cal BC) as

well as from the medieval cemetery of Petersberg/Kleiner Madron (1020–1116 cal AD), all located in

Germany (Figure 1, Figure 1—figure supplements 1–3). After the three aDNA extracts had

appeared HBV-positive in the initial virus screening, they were subjected to deep-sequencing with-

out any prior enrichment resulting in 367 to 419 million reads per sample (Table 1). A principal com-

ponent analysis (PCA) of the human DNA recovered from Karsdorf (3-fold genomic coverage)

revealed that the sample clusters tightly with other contemporary early Neolithic individuals from

the LBK (Figure 1—figure supplement 4). The genetic makeup of the early LBK agriculturalists was

previously found quite distinct from the preceding western hunter-gatherers of Europe. The genetic

shift between both populations was interpreted as a result of early farmers migrating from

Western Anatolia into Central Europe introducing agriculture (Lazaridis et al., 2014; Haak et al.,

2015). The almost 2000 years younger Sorsum individual (1.2-fold genomic coverage) clusters in the

PCA most closely with individuals from the contemporary Funnel Beaker culture that inhabited


Short report Genetics and Genomics Microbiology and Infectious Disease


Northern Germany at the end of the fourth millennium BCE (Figure 1—figure supplement 4). This

population was previously shown to be quite admixed, as a result of a spatial and temporal overlap

of early Neolithic farmers and remaining western hunter-gatherers for almost 2000 years

(Bollongino et al., 2013; Haak et al., 2015). The Petersberg individual (2.9-fold genomic coverage),

Figure 1. Origin of samples. Geographic location of the samples from which ancient HBV genomes were isolated. Radiocarbon dates of the specimens

is given in two sigma range. Icons indicate the sample material (tooth or mummy). HBV genomes obtained in this study are indicated by black frame.


The following figure supplements are available for figure 1:

Figure supplement 1. Skull of the investigated Karsdorf individual 537 is from a male with an age at death of around 25–30 years.


Figure supplement 2. Mandible fragment of the Sorsum individual XLVII 11 analyzed in this study is from a male.


Figure supplement 3. Skull of the analyzed Petersberg individual from grave 820 is from a male with an age at death of around 65–70 years.


Figure supplement 4. Principal Component Analysis (PCA) of the human Karsdorf and Sorsum samples together with previously published ancient

populations projected on 27 modern day West Eurasian populations (not shown) based on a set of 1.23 million SNPs (Mathieson et al., 2015).


Figure supplement 5. Damage plots showing deamination patterns of hg19-specific reads for the HalfUDG-treated libraries of (a) Karsdorf, (b) Sorsum,

(c) Petersberg.


Figure supplement 6. Damage plots showing deamination patterns of HBV-specific reads for the HalfUDG-treated libraries of (a) Karsdorf, (b) Sorsum,

(c) Petersberg.


Figure supplement 7. MS/MS spectrum of the proteotypic HBV-peptide DLLDTASALYR from the HBV-protein external core antigen (residues 58–68).


Figure supplement 8. Principal Component Analysis (PCA) of the human Karsdorf and Sorsum samples together with previously published ancient

populations projected on 27 modern day West Eurasian populations (shown in gray) based on a set of 1.23 million SNPs (Mathieson et al., 2015).


Figure supplement 9. Principal Component Analysis (PCA) of the human Petersberg sample projected on 27 modern day West Eurasian populations

based on a set of 1.23 million SNPs (Mathieson et al., 2015).















however, showed genetic affinities in the PCA with modern day central European populations. All

three ancient human individuals are therefore in agreement with the archeological evidence and

radiocarbon dates for their respective time of origin. Together with typical aDNA damage patterns

(Figure 1—figure supplements 5–6), the human population genetic investigation supports the

ancient origin of the obtained datasets.

For successful HBV genome reconstruction, we mapped all metagenomic sequences to 16 HBV

reference genomes (eight human genotypes (A-H) and 8 NHPs from Africa and Asia) that are repre-

sentative of the current HBV strain diversity (Supplementary file 6). The mapped reads were used

for a de novo assembly, resulting in contigs from which one ancient HBV consensus sequence per

sample was constructed. The consensus genomes are 3161 (46-fold coverage), 3182 (47-fold cover-

age), and 3183 (104-fold coverage) nucleotides in length, which falls in the length range of modern

HBV genomes and suggests that we successfully reconstructed the entire ancient HBV genomes

(Table 1, Figure 2—figure supplements 1–3). Further, when we conducted liquid chromatography-

mass spectrometry (LC-MS) based bottom-up proteomics on tooth material from the three individu-

als, we identified in the Karsdorf and Petersberg samples a peptide that is part of the very stable

HBV core protein, supporting the presence and active replication of HBV in the individuals’ blood

(Figure 1—figure supplement 7).

Phylogenetic network analysis was carried out with a dataset comprised of 493 modern HBV

strains representing the full genetic diversity. Strikingly, the Neolithic HBV genomes did not group

with any human strain in the phylogeny. Instead, they branched off in two lineages and were most

closely related to the African NHP genomes (Figure 2, 93% similarity). Although the two Neolithic

strains were recovered from humans who had lived about 2000 years apart, they showed a higher

genomic similarity to each other than to any other human or NHP genotype. Still, their genomes dif-

fered by 6% from each other and may therefore be considered representatives of two separate line-

ages. They did, however, differ less than 8% from the African NHP strains and should therefore not

be called a separate genotype (Figure 2—figure supplement 4). The genome from the 1000-year-

old Petersberg individual clustered with modern D4 genotypes.

Owing to continuous recombination over time, different gene segments or modules of the ances-

tral genomes can show up in various subsequent virus generations. Such precursors have been pos-

tulated (Simmonds and Midgley, 2005) and their existence is supported by the results of our

recombination analysis (Figure 2—figure supplements 5–8, Figure 2—source data 1). Some frag-

ments of the Karsdorf sequences appeared to be very similar to modern human (G, E) and African

NHP genotypes, and the Sorsum genome partially showed a high similarity to the human genotypes

G, E and B. (Figure 2—figure supplements 5–8, Figure 2—source data 1). Given the close relation-

ship between the two Neolithic virus genomes, it is also conceivable that the older HBV from Kars-

dorf could have been a distant source for the younger Sorsum virus (Figure 2—figure supplements

5–8, Figure 2—source data 1). The closer relationship between the Neolithic and the NHP strains

compared to other human strains is noteworthy and may have involved reciprocal cross-species

transmission at one or possibly several times in the past (Simmonds and Midgley, 2005;

Souza et al., 2014; Rasche et al., 2016).

Taken together, our results demonstrate that HBV already existed in Europeans 7000 years ago

and that its genomic structure closely resembled that of modern hepatitis B viruses. Both Neolithic

Table 1. Results of the genome reconstruction

*Mergedreads

Length of HBVconsensussequence

Mean HBVcoverage

Gaps in the consensussequence at nt position

*Mappedreads HBV

*Mappedreadshuman

Meanhumancoverage

Humangenomes/HBVgenomes

Karsdorf 386,780,892 3183 104X 2157–2175; 3107–3128; 3133–3183

10,718 122,568,310 2.96X 1: 35.1

Sorsum 367,574,767 3182 47X - 3249 9,856,001 1.17X 1: 40.2

Petersberg 419,413,082 3161 46X 880–1000; 1232–1329; 1331–1415; 1420–1581; 1585–1598

2125 105,476,677 2.88X 1: 16

*number.

nt, nucleotide.






Figure 2. Network. Network of 493 modern, two published ancient genomes (light yellow box), and three ancient

hepatitis B virus (HBV) obtained in this study (grey box). Colors indicate the eight human HBV genotypes (A–H),

two monkey genotypes (Monkeys I, African apes and Monkeys II, Asian monkeys) and ancient genomes (red).


The following source data and figure supplements are available for figure 2:

Source data 1. Results of the recombination analysis using the methods RDP, GENECOV, Chimera, MaxChi, Boot-

Scan, SiScan, 3Seq within the RDP v4 software package with all modern full reference genomes (n = 493) and five

ancient genomes.


Source data 2. Multiple sequence alignment of the 493 representative and five ancient HBV genomes.


Source data 3. Maximum-likelihood tree based on the multiple sequence alignment of the 493 representative and

five ancient HBV genomes with 2000 replicates.


Source data 4. Neighbour-Joining tree based on the multiple sequence alignment of the 493 representative mod-

ern and five ancient HBV genomes with 10000 replicates.


Figure supplement 1. Consensus sequence of the Karsdorf HBV genome.


Figure supplement 2. Consensus sequence of the Sorsum HBV genome.


Figure supplement 3. Consensus sequence of the Petersberg HBV genome.


Figure supplement 4. Genetic (hamming) distance of our three ancient HBV genomes compared to all 493

reference genomes.


Figure supplement 5. BootScan analysis of the sequence Karsdorf.


Figure supplement 6. BootScan analysis of the sequence Sorsum.


Figure supplement 7. BootScan analysis of the sequence Petersberg.


Figure supplement 8. SimPlot analysis of (a) Karsdorf, (b) Sorsum and (c) Petersberg.


Figure 2 continued on next page

















viruses fall between the present-day modern human and the known NHP diversity. Therefore, it can

be hypothesized that although the two Neolithic HBV strains are no longer observed today and thus

may reflect two distinct clades that went extinct, they could still be closely related to the remote

ancestors of the present-day genotypes, which is supported by signs of ancient recombination

events. More ancient precursors, intermediates and modern strains of both humans and NHPs need

to be sequenced to disentangle the complex evolution of HBV. As this evolution is characterized by

recombination and point mutations and may further be complicated by human-ape host barrier

crossing (Simmonds and Midgley, 2005; Souza et al., 2014; Rasche et al., 2016), genetic dating is

not expected to yield meaningful results. This is additionally supported by a TempEst analysis

(Rambaut et al., 2016) that shows very little temporal signal (Figure 2—figure supplement 9). It

should, however, be noted that the oldest genome (Karsdorf) was found in an individual that

belonged to a population of early farmers that had migrated in the previous few hundred years from

the Near East into central Europe. One might speculate that the close proximity to recently domesti-

cated animals, changes in subsistence strategy as well as the adopted sedentary lifestyle might have

contributed to the spread of HBV within Neolithic human populations.

Based on our analysis, HBV DNA can reliably be detected in tooth samples that are up to 7000

years old. Ancient HBV has so far only been identified in soft tissue from two 16th-century mummies

(Kahila Bar-Gal et al., 2012; Patterson Ross et al., 2018). The aDNA analysis of HBV from prehis-

toric skeletons, which facilitates evolutionary studies on a far-reaching temporal scale, has not been

described up to now. One explanation for the difficulty of a molecular HBV diagnosis in bones is

that the virus infection does not leave lesions on skeletal remains that would allow researchers to

select affected individuals a priori, as it is the case for instance for leprosy (Schuenemann et al.,

2013). The diagnosis of an HBV infection in skeletal populations is purely a chance finding and is

thus more probable in a large-scale screening.

Overall, HBV biomolecules seem to be well preserved in teeth: Avoiding biases from DNA cap-

ture and reference-based mapping we could reconstruct three HBV genomes by de novo assembly

from shotgun data and even observed HBV-specific peptides. The ratio of HBV genomes to the

human genome in our samples was rather high and similar in all three samples (Karsdorf 35:1, Sor-

sum 40.2:1 and Petersberg 16:1). As there is no evidence that HBV DNA is more resistant to post-

mortem degradation than human DNA, the high rate of HBV compared to human DNA may reflect

the disease state in the infected individuals at the time of death. High copy numbers of viral DNA in

the blood of infected individuals are associated with acute HBV infection, or reactivation of chronic

HBV. Thus, it seems likely that the death of the ancient individuals is related to the HBV infection,

but might not be the direct cause of death as fulminant liver failure is rather rare in modern day

patients. The HBV infection might have instead contributed to other forms of lethal liver failure such

as cirrhosis or liver cancer.

In view of the unexpected complexity of our findings, we envisage future diachronic HBV studies

that go beyond the temporal and geographic scope of our current work.

Materials and methods

Human remainsThe LBK settlement of Karsdorf, Saxony-Anhalt, Germany, is located in the valley of the river Unstrut.

Between 1996 and 2010 systematic excavations were conducted at Karsdorf that led to the discovery

of settlements and graves from the Neolithic to the Iron Age (Behnke, 2007; 2011; 2012). The LBK

is represented by 24 longhouses in north-west to south-east orientation that were associated with

settlement burials (Veit, 1996). The investigated individual 537 is a male with an age at death of

around 25–30 years (Figure 1—figure supplement 1), dated to 5056–4959 cal BC (KIA 40357–

6116 ± 32 BP) (Brandt et al., 2014; Nicklisch, 2017).

Figure 2 continued

Figure supplement 9. Plot of phylogenetic root-to-tip distance relative to sampling time (TempEst).






The gallery grave of Sorsum, Lower-Saxony, Germany, is typologically dated to the Tiefstichkera-

mik (group of the Funnelbeaker culture). Sorsum is exceptional as it was built into the bedrock. Dur-

ing the excavations (1956–1960) of the grave chamber around 105 individuals were recovered

(Claus, 1983; Czarnetzki, 1966). Individual XLVII 11 analyzed in this study is a male (Figure 1—fig-

ure supplement 2) and dates to 3335–3107 cal BC (MAMS 33641–4501 ± 19 BP).

The medieval cemetery of Petersberg/Kleiner Madron, Bavaria, Germany, lies on a hill top at 850

meters asl and 400 meters above the floor of the Inn Valley. On the eastern part of the cemetery

members of a priory were buried that was most likely established in the late 10th century. Written

sources document its existence from 1132 onwards (Meier, 1998). During systematic excavations

(1997–2004) in the southeastern part of the churchyard the remains of individuals buried in 99 graves

were uncovered. The examined individual in grave 820 is a male with an age at death of around 65–

70 years (Losch, 2009 - Figure 1—figure supplement 3) dating to 1020–1116 cal AD (MAMS

33642–982 ± 17 BP).

DNA extraction and sequencingThe DNA extractions and pre-PCR steps were carried out in clean room facilities dedicated to aDNA

research. Teeth were used for the analyses. The samples from Petersberg and Sorsum were proc-

essed in the Ancient DNA Laboratory at Kiel University and the sample from Karsdorf in the Ancient

DNA Laboratory of the Max Planck Institute for the Science of Human History (MPI SHH) in Jena. All

procedures followed the guidelines on contamination control in aDNA studies (Warinner et al.,

2017; Key et al., 2017). The teeth were cleaned in pure bleach solution to remove potential con-

taminations prior to powdering. Fifty milligrams of powder were used for extraction following a sil-

ica-based protocol (Dabney et al., 2013). Negative controls were included in all steps.

From each sample, double-stranded DNA sequencing libraries (UDGhalf) were prepared accord-

ing to an established protocol for multiplex high-throughput sequencing (Meyer and Kircher,

2010). Sample-specific indices were added to both library adapters via amplification with two index

primers. Extraction and library blanks were treated in the same manner. For the initial screening, the

library of the individual from Karsdorf was sequenced on 1/50 of a lane on the HiSeq 3000 (2 � 75

bp) at the MPI SHH in Jena and the libraries from Petersberg and Sorsum were sequenced on the

Illumina HiSeq 4000 (2 � 75 bp) platform at the Institute of Clinical Molecular Biology, Kiel Univer-

sity, using the HiSeq v4 chemistry and the manufacturer’s protocol for multiplex sequencing. Deep-

sequencing for each of the three samples was carried out on two lanes on the Illumina HiSeq 4000

platform at the Institute of Clinical Molecular Biology, Kiel University.

Clip and mergeThe datasets produced for all ancient samples contained paired-end reads with varying numbers of

overlapping nucleotides as well as artificial adapter sequences. We used ClipAndMerge version

1.7.3, a module of the EAGER pipeline (Peltzer et al., 2016), to clip adapter sequences, merge cor-

responding paired-end reads in overlapping regions and to trim the resulting reads. We used the

default options with the following command:

java -jar ClipAndMerge.jar -in1 $FASTQ1 -in2 $FASTQ2 \

-f AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC \

-r AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA \

-l 25 -qt -q 20 -o $output_file

where $FASTQ1 and $FASTQ2 are the two gzipped FASTQ input files

Adapter clippingClipAndMerge uses an overlap alignment of the respective forward or reverse adapter with the 3’

end of each read in order to remove sequencing adapter sequences. Regions at the 3’ end of each

read that were contained in the alignment were clipped. Reads that were shorter than 25 nucleoti-

des after adapter clipping or contained only adapter sequences (adapter dimers) were removed. All

remaining reads were then used in the merging step.




Merging of overlapping paired readsMerging was performed for all remaining paired reads with a minimum overlap of 10 nucleotides

and at most 5% mismatches in the overlap region. The algorithm selected the maximal overlap fulfill-

ing these criteria. The consensus sequence was generated using the nucleotides in the overlap

regions from the read with the higher PHRED quality score, maximizing the quality of the resulting

read.

Quality trimmingIn a final step, ClipAndMerge performed quality trimming of the reads and all nucleotides with

PHRED scores smaller than 20 were trimmed from the 3’ end of each read. Finally, all reads with

fewer than 25 nucleotides after quality trimming were removed. The resulting high-quality reads

were used for the alignment.

Virus screeningScreening of the datasets was carried out with the software MALT using the ncbi-viral database as a

reference. A sequence identity threshold of 85% was set and the alignment mode was changed to

SemiGlobal. The analysis was carried out using the following command:

malt-run –mode BlastN -e 0.001 -id 85 –alignmentType SemiGlobal –index $index –inFile

$FASTQCM –output $OUT

where $index is the index file, $FASTQCM is the clipped and merged file and $OUT is the output

the file.

The resulting alignments were visually inspected using MEGAN 6. Reads mapping to the hepatitis

B reference in the database (NC_003977.2) were extracted and verified using a discontiguous mega-

blast against the virus taxa (taxid: 10239) with default parameters.

HBV alignmentFor identification of the genotypes, samples were aligned against one reference for each of the eight

hepatitis B genotypes available in the NCBI hepatitis B genotyping project (https://www.ncbi.nlm.

nih.gov/projects/genotyping/view.cgi?db=2) (Supplementary file 1). Additionally, eight NHP strains

were used. All references were combined in one FASTA file and a competitive mapping was per-

formed using BWA. The mapping was carried out using the following command:

bwa aln -n 0.01 l 300 $INDEX $FASTQCM $OUT

where $INDEX is the reference, $FASTQCM is the input file and $OUT is the output file.

Minimum mapping quality was set to 0.

Duplicate removalWe used DeDup version 0.11.3, part of the EAGER pipeline,Peltzer et al., 2016 to identify and

remove all duplicate reads in the sample specific BAM files (Supplementary file 2) with the default

options and the following command:

java -jar DeDup.jar -i $IN -o $OUT

where $IN is the input BAM file and $OUT is the output BAM file.

Extracting mapped readsAfter duplicate removal the resulting BAM files were converted to SAM files using SAMtools version

0.1.19-96b5f2294a with default parameters and the following command:

samtools view -h -o $OUT $IN

where $OUT is the SAM output file and $IN is the BAM input file. Reads from the SAM file where

converted to FASTQ using the following awk script:

awk ’/[FMR]/{print ‘@”$1’\n’$10’\n+\n’$11}’ $IN > $OUT where $IN is a SAM file and $OUT is the

resulting FASTQ file containing all the mapped reads.

De novo assemblyThe de novo assembly was performed using the SPAdes genome assembler version v3.9.0

(Bankevich et al., 2012) with the following settings:

spades.py -t 20 m 500 k



https://www.ncbi.nlm.nih.gov/projects/genotyping/view.cgi?db=2

https://www.ncbi.nlm.nih.gov/projects/genotyping/view.cgi?db=2


11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,

73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,

127 s $IN -o $OUT

where $IN is a FASTQ file containing the mapped reads and $OUT is the output folder for

SPAdes.

Resulting contigs for each K-value were checked and the one which spawned the longest contig

was selected for further processing (Supplementary file 3).

Mapping of contigsContigs were mapped against the multi FASTA file containing all 16 references. The following com-

mand was used:

bwa mem $INDEX $IN $OUT

where $INDEX is the reference, $IN the file containing the contig/contigs and $OUT is the result-

ing BAM file.

Consensus generationFor genomic reconstruction of the ancient HBV strains, the results of the alignments were inspected

visually with IGV version 2.3.92 (Thorvaldsdottir et al., 2013). Information about contig order and

direction were used for the construction of a consensus sequence. Bases that were soft clipped in

the alignment were cut off using SeqKit software version 0.7.0 and realigned to the 16 references as

described above. This was done because of the circular genome structure of HBV. Big contigs

needed to be split to preserve genomic order with respect to the reference sequences

(Supplementary file 4).

Remapping raw reads against the consensus sequenceRaw reads of each sample were mapped to their corresponding consensus sequence using the soft-

ware CircularMapper version 1.93.4 and the following command line:

java -jar CircularGenerator.jar -e $E -i $IN -s ‘$N’

where $E is the length of elongation, $IN is the input file and $N is the name of the target

sequence.

bwa aln -t 8 $IN $R -n 0.01-l 300-f $OUT

where $IN is the elongated consensus sequence, $R is the file containing the clipped and merged

reads and $OUT is the output file.

bwa samse $RE $IN $R -f $OUT

where $RE is the elongated reference, $IN is the bwa aln output, $ R is the file containing the

clipped and merged reads and $OUT is the output file.

java -jar realign-1.93.4.jar -e $E -i $IN -r $OR

where $E is the length of elongation, $IN is the output of bwa samse and $OR is the unmodified

consensus.

Phylogenetic analysisHepatitis B reference strains for apes were collected using edirect with the following command:

esearch -db pubmed -query ‘hepatitis B AND Orangutan OR hepatitis B AND Gibbon OR hepati-

tis B AND Gorilla OR hepatitis B AND Chimpanzee OR hepatitis B AND Orang-utan’ | elink -target

nuccore | efetch -format fasta > $OUT

where $OUT is the output file in fasta format containing all sequences from the papers containing

the search keys.

To control the received sequences a multiple sequence alignment using the linsi algorithm con-

tained in MAFFT version 7.310 was carried out. The following command was used:

linsi $IN > $OUT

where $IN is the input file containing the retrieved sequences and $OUT is the multiple sequence

alignment.

The alignment was visually inspected in AliView (v. 1.18.1) and sequences that differed from the

majority were removed. This step was necessary due to the unrestricted esearch command which, by




chance, could also return non-primate sequences. After filtering the set contained 74 ape infecting

HBV strains.

Using the 74 ape strains and 5497 non-recombinant genomes available at hpvdb (https://hbvdb.

ibcp.fr/HBVdb/HBVdbDataset?seqtype=0) clustering was carried out with UClust v 1.1.579

(Edgar, 2010). The clustering with an identity threshold of 97% yielded 493 representative HBV

genomes. Combining them with the five ancient strains a multiple sequence alignment was carried

out using Geneious version 10.1.2 (Kearse et al., 2012) with a 65% similarity cost matrix, a gap

open penalty of 12 and a gap extension penalty of 3. The multiple sequence alignment was stripped

of any sites (columns) that had gaps in more than 95% of sequences. The complete alignment includ-

ing all modern and ancient genomes as multi-fasta is available in Figure 2—source data 2. The

alignment was used to construct a network with the software SplitsTree v4 (Huson and Bryant,

2006), creating a NeighborNet (Bryant and Moulton, 2004) with uncorrected P distances.

The same multiple sequence alignment was used for the generation of Maximum-Likelihood (ML)

and Neighbour-Joining (NJ) Trees. MEGA7 version 7170509-x86_64 with the following command

line was used:

Megacc -a $MAO -d $IN -o $OUT

where $MAO is the megacc configuration file, $IN is the multiple alignment and $OUT is the out-

put directory. For both trees 1408 informative sites and Jukes-Cantor substitution model were used.

Bootstrap replicates are 2000 for ML and 10000 for NJ. The trees are provided in Figure 2—source

data 3 and 4.

Molecular clock analysisThe evolution of hepatitis B virus over time is unclear with regard to its evolutionary rate and the

role of recombination. Previous studies have attempted to detect a molecular clock-like signature

without success. We investigate if the ancient genomes presented here allow a molecular clock anal-

ysis using TempEst v1.5.1 (Rambaut et al., 2016). The data set shows little positive correlation

between genetic divergence and sampling time (correlation coefficient 0.075) and there is very little

temporal signal (TempEst R2 = 0.006, see Figure 2—figure supplement 9). Therefore, we refrain

from further dating analysis.

Recombination analysisWe performed recombination analysis using all modern full reference genomes (n = 493) and five

ancient genomes used for the network analysis (see above in Phylogenetic analysis). The methods

RDP, GENECOV, Chimera, MaxChi, BootScan, SiScan, 3Seq within RDP v4 (Martin et al., 2015) with

a window size of 100 nt and the parameter set to circular genome with and without outgroup refer-

ence (results are provided in Figure 2—source data 1) and SimPlot v 3.5.1 (Lole et al., 1999, Fig-

ure 2—figure supplements 5–8) were applied to the data set.

LC-MS/MS analysis and database searchesProteins were extracted from powdered tooth samples (50 mg) using a modified filter-aided sample

preperation (FASP) protocol as previously described (Warinner et al., 2014; Cappellini et al.,

2014). In-filter trypsin digested samples were analyzed on a Dionex Ultimate 3000 nano-HPLC cou-

pled to a Q Exactive mass spectrometer (Thermo Scientific, Bremen). The samples were washed on

a trap column (Acclaim Pepmap 100 C18, 10 mm �300 mm, 3 mm, 100 A, Dionex) for 5 min with 3%

acetonitrile (ACN)/0.1% TFA at a flow rate of 30 mL/min prior to peptide separation on an Acclaim

PepMap 100 C18 analytical column (15 cm �75 mm, 3 mm, 100 A, Dionex). A flow rate of 300 nL/min

using eluent A (0.05% formic acid (FA)) and eluent B (80% ACN/0.04% FA) was used for gradient

separation as follows: linear gradient 5 ± 50% B in 60 min, 50 ± 95% B in 5 min, 95% B for 10 min, 95

± 5% B in 1 min, and equilibration at 5% B for 12 min. Spray voltage applied on a metal-coated Pico-

Tip emitter (30 mm tip size, New Objective, Woburn, Massachusetts, US) was 1.25 kV, with a source

temperature of 250˚C. Full scan MS spectra were acquired from 5 to 145 min between 300 and

2,000 m/z at a resolution of 60,000 at m/z 400 (automatic gain control [AGC] target of 1E6; maxi-

mum ion injection time [IIT] of 500 ms). The five most intense precursors with charge states 2 + used

were selected with an isolation window of 1.6 m/z and fragmented by HCD with normalized collision



https://hbvdb.ibcp.fr/HBVdb/HBVdbDataset?seqtype=0

https://hbvdb.ibcp.fr/HBVdb/HBVdbDataset?seqtype=0


energies of 25. The precursor mass tolerance was set to 10 ppm, and dynamic exclusion (30 s) was

enabled.

Acquired spectra were analyzed by database searches using Proteome Discoverer (PD) 2.2.0.388

with the search engines SequestHT (Thermo Scientific). Searches were performed against a com-

bined database built by the combination of the full Swiss protein database (468,716 entries, down-

loaded from Uniprot, December, 21st, 2017), hepatitis B data base (seven entries, downloaded from

Uniprot, December, 7th, 2017) and common laboratory contaminants (115 entries, downloaded from

Uniprot, August, 15th, 2014). The following settings were used for the search: semi-tryptic specificity;

two missed cleavage sites; mass tolerances of 10 ppm for precursors and for fragment masses 0.02

Da (HCD) and 0.5 Da (CID); static modifications: carbamidomethylation on Cys; dynamic modifica-

tions: oxidation of Met, Lys and Pro. An additional search was performed using 12 FASTA files from

in silico translated DNA sequences. The DNA sequences were obtained from previous DNA

sequencing of the samples.

A nearly complete y-ion series and two b-ion fragments allow for an assignment of the full pep-

tide sequence. The peptide was identified in the biological sample from Petersberg with four pep-

tide spectral matches, showing that the detection of this peptide is not a random event. Moreover,

the same peptide could also be identified in the second biological sample from Karsdorf (not

shown); blank runs between the LC-MS/MS runs of the two samples rule out potential artifacts due

to sample carryover.

Note that the MS/MS method applied here does not allow us to distinguish leucine (L) or isoleu-

cine (I) residues. Manual permutation of the leucine residues in the above stated sequence followed

by a BLAST search (default search parameters) led to the identification of the HBV-protein external

core antigen in all cases with the exception of the combinations DIIDTASALYR and DLLDTASAIYR;

these two variants were reported by BLAST search as the proteins hypothetical protein

CR988_04570 [Treponema sp.] and anti-GFP antibody [synthetic construct] with the HBV-protein

external core antigen listed at rank 3. However, these proteins were not found in the genomic data.

Hence, despite the uncertainty of the I/L assignment, the MS/MS data support the genomic finding

of an HBV infection.

Human population genetic analysesMapping of the adapter-clipped and merged FASTQ files to the human reference genome hg19 was

done using BWA (Li and Durbin, 2010) with the following command line:

bwa aln -n 0.01 l 300 $INDEX $FASTQCM $OUT

where $INDEX is the reference, $FASTQCM is the input file and $OUT is the output file. The

duplicate removal after mapping was executed as described above.

The mapped sequencing data were transformed into the Eigenstrat format (Price et al., 2006)

and merged with a dataset of 1,233,013 SNPs (Haak et al., 2015; Mathieson et al., 2015). Using

the software Smartpca (Patterson et al., 2006) the three samples and previously published ancient

populations were projected onto a base map of genetic variation calculated from 32 West Eurasian

populations (Figure 1—figure supplements 4, 8 and 9).

Sex determinationSex was assessed based on the ratio of sequences aligning to the X and Y chromosomes compared

to the autosomes (Skoglund et al., 2013).

AcknowledgementsWe are grateful to the following people and institutions for providing samples, support, and advice:

Bodo Krause-Kyora, Hildegard Nelson (Referat A1 Archaologische Dokumentation, Niedersach-

sisches Landesamt fur Denkmalpflege), Ulrike Weller (Sammlungsverwaltung Archaologie Landesmu-

seum Hannover) and Britta Steer for technical assistance with proteomics sample preparation. This

work was supported by the Collaborative Research Centre 1266 Scales of Transformation, the Excel-

lence Cluster 306 Inflammation at Interfaces, the Medical Faculty of Kiel University, the Max Planck

Society and the European Research Council (ERC) starting grant APGREID (to JK). Excavations and

analysis of the archaeological site of Karsdorf were supported by the German Research Foundation

(DFG) Grant of Kurt W Alt (Al 287-7-1) and Harald Meller (Me 3245/1–1). Analysis of the




archaeological site of Petersberg/Kleiner Madron was supported by a grant of the VolkswagenStif-

tung to Thomas Meier and by a PhD-Fellowship of the Ludwig-Maximilians-University, Munich to

Sandra Losch.

Additional information

Funding

Funder Grant reference number Author

Collaborative Research Center 1266 Ben Krause-KyoraAlmut Nebel

Swiss National Science Foun-dation

PMPDP3_171320/1 Denise Kuhnert

Deutsche Forschungsge-meinschaft

HE7987/1-1 Henrike O Heyne


Me 3245/1-1 Harald Meller


Al 287-7-1 Kurt W Alt

European Research Council APGREID Johannes Krause

The funders had no role in study design, data collection and interpretation, or the

decision to submit the work for publication.

Author contributions

Ben Krause-Kyora, Conceptualization, Resources, Data curation, Formal analysis, Supervision, Fund-

ing acquisition, Validation, Investigation, Methodology, Writing—original draft, Project administra-

tion, Writing—review and editing; Julian Susat, Felix M Key, Alexander Herbig, Data curation,

Formal analysis, Investigation, Writing—original draft, Writing—review and editing; Denise Kuhnert,

Esther Bosse, Formal analysis, Investigation; Alexander Immel, Formal analysis, Investigation, Writ-

ing—original draft; Christoph Rinne, Harald Meller, Susanne Friederich, Stefan Schreiber, Resources;

Sabin-Christin Kornell, Investigation; Diego Yepes, Soren Franzenburg, Formal analysis; Henrike O

Heyne, Formal analysis, Writing—original draft; Thomas Meier, Sandra Losch, Resources, Investiga-

tion; Nicole Nicklisch, Resources, Formal analysis, Investigation; Kurt W Alt, Resources, Formal analy-

sis; Andreas Tholey, Resources, Supervision, Methodology, Writing—original draft; Almut Nebel,

Resources, Formal analysis, Funding acquisition, Writing—original draft, Writing—review and edit-

ing; Johannes Krause, Conceptualization, Resources, Data curation, Supervision, Funding acquisition,

Methodology, Writing—original draft, Project administration, Writing—review and editing

Author ORCIDs

Ben Krause-Kyora http://orcid.org/0000-0001-9435-2872

Felix M Key http://orcid.org/0000-0003-2812-6636

Johannes Krause http://orcid.org/0000-0001-9144-3920

Ethics

Human subjects: The human remains are prehistoric European specimens, so consent was not

required. No decedent groups claim responsibility or ancestry to those people.

Decision letter and Author response

Decision letter https://doi.org/10.7554/eLife.36666.037

Author response https://doi.org/10.7554/eLife.36666.038



http://orcid.org/0000-0001-9435-2872

http://orcid.org/0000-0003-2812-6636

http://orcid.org/0000-0001-9144-3920




Additional files

Supplementary files. Supplementary file 1. Accession numbers for the reference genomes used in the first alignment

step to catch HBV diversity in the sample. Since monkey HBV strains are not classified into geno-

types the column is left blank.


. Supplementary file 2. Number of reads mapping against the references shown in

Supplementary file 1 before and after duplicate removal.


. Supplementary file 3. Number of contigs and combined contig length of the de novo assembly for

chosen K-values.


. Supplementary file 4. Final consensus length after retrieving gap information from the multiple

sequence alignment with Geneious.


. Supplementary file 5. Number of reads mapping against hg19 before and after duplicate removal

and percentage of the genome where coverage is at least one.


. Supplementary file 6. Basic statistics for the mapping against the references shown in table S1.

Shown are mean coverage, mean coverage for the covered region, genome length, number of miss-

ing bases and covered bases


. Transparent reporting form


Data availability

Raw sequence read files have been deposited at the European Nucleotide Archive under accession

no. PRJEB24921

The following dataset was generated:

Author(s) Year Dataset title Dataset URL

Database, license,and accessibilityinformation

Krause J 2018 High-throughput sequence data forNeolithic and Medieval virusgenomes reveal complex evolutionof Hepatitis B

https://www.ebi.ac.uk/ena/data/view/PRJEB24921

Publicly available atthe EuropeanNucleotide Archive(accession no.PRJEB24921)

ReferencesAraujo NM. 2015. Hepatitis B virus intergenotypic recombinants worldwide: an overview. Infection, Genetics andEvolution 36:500–510. DOI: https://doi.org/10.1016/j.meegid.2015.08.024

Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, PrjibelskiAD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. 2012. SPAdes: a new genomeassembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology 19:455–477. DOI: https://doi.org/10.1089/cmb.2012.0021, PMID: 22506599

Behnke HJ. 2007. Su€ßes aus der Bronzezeit – Siedlungsgunst im Unstruttal bei Karsdorf, Burgenlandkreis. DieAusgrabungen 2004. In: Archaologie in Sachsen-Anhalt N.F. 4 p. 63–86.

Behnke HJ. 2011. Erste Siedler der Linienbandkeramik in der Karsdorfer Feldflur. Ergebnisse der Ausgrabungenim Jahr 2005. In: Archaologie in Sachsen-Anhalt N.F. 5 p. 184–199.

Behnke HJ. 2012. Siedlungsgunst im Unstruttal bei Karsdorf, Burgenlandkreis: Ergebnisse der Grabungen 2006und 2007. In: Archaologie in Sachsen-Anhalt N. F. 6 p. 35–70.

Bollongino R, Nehlich O, Richards MP, Orschiedt J, Thomas MG, Sell C, Fajkosova Z, Powell A, Burger J. 2013.2000 years of parallel societies in stone age central Europe. Science 342:479–481. DOI: https://doi.org/10.1126/science.1245049, PMID: 24114781













https://doi.org/10.1016/j.meegid.2015.08.024

https://doi.org/10.1089/cmb.2012.0021

http://www.ncbi.nlm.nih.gov/pubmed/22506599

https://doi.org/10.1126/science.1245049




Bouckaert R, Alvarado-Mora MV, Pinho JR. 2013. Evolutionary rates and HBV: issues of rate estimation withBayesian molecular methods. Antiviral Therapy 18:497–503. DOI: https://doi.org/10.3851/IMP2656, PMID: 23792904

Brandt G, Knipper C, Nicklisch N, Ganslmeier R, Klamm M, Alt KW. 2014. Settlement burials at the Karsdorf LBKsite, Saxony-Anhalt, Germany - biological ties and residential mobility. In: Whittle A, Bickle Penny (Eds). EarlyFarmers. The View from Archaeology and Science. Oxford University Press, British Academy. p. 95–114.

Bryant D, Moulton V. 2004. Neighbor-net: an agglomerative method for the construction of phylogeneticnetworks. Molecular Biology and Evolution 21:255–265. DOI: https://doi.org/10.1093/molbev/msh018,PMID: 14660700

Cappellini E, Gentry A, Palkopoulou E, Ishida Y, Cram D, Roos A-M, Watson M, Johansson US, Fernholm B,Agnelli P, Barbagli F, Littlewood DTJ, Kelstrup CD, Olsen JV, Lister AM, Roca AL, Dalen L, Gilbert MTP. 2014.Resolution of the type material of the Asian elephant, Elephas maximus Linnaeus, 1758 (Proboscidea,Elephantidae). Zoological Journal of the Linnean Society 170:222–232. DOI: https://doi.org/10.1111/zoj.12084

Castelhano N, Araujo NM, Arenas M. 2017. Heterogeneous recombination among Hepatitis B virus genotypes.Infection, Genetics and Evolution 54:486–490. DOI: https://doi.org/10.1016/j.meegid.2017.08.015, PMID: 28827173

Claus M. 1983. Das neolithische Felsenkammergrab auf dem Halsberg bei Sorsum, Stadt Hildesheim. Die KundeN. F. :91–122.

Czarnetzki A. 1966. Die menschlichen Skelettreste aus vier neolithischen Steinkisten Hessens undNiedersachsens. Tubingen: Ungedr. Diss. Univ. Tubingen.

Dabney J, Knapp M, Glocke I, Gansauge MT, Weihmann A, Nickel B, Valdiosera C, Garcıa N, Paabo S, ArsuagaJL, Meyer M. 2013. Complete mitochondrial genome sequence of a middle pleistocene cave bearreconstructed from ultrashort DNA fragments. PNAS 110:15758–15763. DOI: https://doi.org/10.1073/pnas.1314445110, PMID: 24019490

Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.DOI: https://doi.org/10.1093/bioinformatics/btq461, PMID: 20709691

Haak W, Lazaridis I, Patterson N, Rohland N, Mallick S, Llamas B, Brandt G, Nordenfelt S, Harney E, StewardsonK, Fu Q, Mittnik A, Banffy E, Economou C, Francken M, Friederich S, Pena RG, Hallgren F, Khartanovich V,Khokhlov A, et al. 2015. Massive migration from the steppe was a source for Indo-European languages inEurope. Nature 522:207–211. DOI: https://doi.org/10.1038/nature14317, PMID: 25731166

Huson DH, Bryant D. 2006. Application of phylogenetic networks in evolutionary studies. Molecular Biology andEvolution 23:254–267. DOI: https://doi.org/10.1093/molbev/msj030, PMID: 16221896

Kahila Bar-Gal G, Kim MJ, Klein A, Shin DH, Oh CS, Kim JW, Kim TH, Kim SB, Grant PR, Pappo O, Spigelman M,Shouval D. 2012. Tracing hepatitis B virus to the 16th century in a Korean mummy. Hepatology 56:1671–1680.DOI: https://doi.org/10.1002/hep.25852, PMID: 22610996

Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C,Thierer T, Ashton B, Meintjes P, Drummond A. 2012. Geneious basic: an integrated and extendable desktopsoftware platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649.DOI: https://doi.org/10.1093/bioinformatics/bts199, PMID: 22543367

Key FM, Posth C, Krause J, Herbig A, Bos KI. 2017. Mining metagenomic data sets for ancient DNA:recommended protocols for authentication. Trends in Genetics 33:508–520. DOI: https://doi.org/10.1016/j.tig.2017.05.005, PMID: 28688671

Lazaridis I, Patterson N, Mittnik A, Renaud G, Mallick S, Kirsanow K, Sudmant PH, Schraiber JG, Castellano S,Lipson M, Berger B, Economou C, Bollongino R, Fu Q, Bos KI, Nordenfelt S, Li H, de Filippo C, Prufer K,Sawyer S, et al. 2014. Ancient human genomes suggest three ancestral populations for present-day Europeans.Nature 513:409–413. DOI: https://doi.org/10.1038/nature13673, PMID: 25230663

Li H, Durbin R. 2010. Fast and accurate long-read alignment with burrows-wheeler transform. Bioinformatics 26:589–595. DOI: https://doi.org/10.1093/bioinformatics/btp698, PMID: 20080505

Littlejohn M, Locarnini S, Yuen L. 2016. Origins and evolution of hepatitis B virus and hepatitis D virus. ColdSpring Harbor Perspectives in Medicine 6:a021360. DOI: https://doi.org/10.1101/cshperspect.a021360,PMID: 26729756

Lole KS, Bollinger RC, Paranjape RS, Gadkari D, Kulkarni SS, Novak NG, Ingersoll R, Sheppard HW, Ray SC.1999. Full-length human immunodeficiency virus type 1 genomes from subtype C-infected seroconverters inIndia, with evidence of intersubtype recombination. Journal of Virology 73:152–160. PMID: 9847317

Losch S. 2009. Palaopathologisch-anthropologische und molekulare Untersuchungen an mittelalterlichen undfru€hneuzeitlichen Bevolkerungsgruppen. Dissertation zum Erwerb des Doktorgrades der Humanbiologie an derMedizinischen Fakultat der Ludwig-Maximilians-Universitat zu Mu€nchen. Medizinischen Fakultat der UniversitatMunchen.

Martin DP, Murrell B, Golden M, Khoosal A, Muhire B. 2015. RDP4: detection and analysis of recombinationpatterns in virus genomes. Virus Evolution 1:vev003. DOI: https://doi.org/10.1093/ve/vev003, PMID: 27774277

Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, Harney E, Stewardson K, FernandesD, Novak M, Sirak K, Gamba C, Jones ER, Llamas B, Dryomov S, Pickrell J, Arsuaga JL, de Castro JM,Carbonell E, Gerritsen F, et al. 2015. Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528:499–503. DOI: https://doi.org/10.1038/nature16152, PMID: 26595274

Meier T. 1998. Ein mittelalterliches Kloster mit Friedhof auf dem Kleinen Madron/Petersberg bei Flintsbach a.Inn. Landkreis Rosenheim, Oberbayern. In: Das Archaologische Jahr in Bayern. p. 127–129.



https://doi.org/10.3851/IMP2656



https://doi.org/10.1093/molbev/msh018


https://doi.org/10.1111/zoj.12084

https://doi.org/10.1016/j.meegid.2017.08.015



https://doi.org/10.1073/pnas.1314445110

https://doi.org/10.1073/pnas.1314445110


https://doi.org/10.1093/bioinformatics/btq461


https://doi.org/10.1038/nature14317


https://doi.org/10.1093/molbev/msj030


https://doi.org/10.1002/hep.25852


https://doi.org/10.1093/bioinformatics/bts199


https://doi.org/10.1016/j.tig.2017.05.005

https://doi.org/10.1016/j.tig.2017.05.005




https://doi.org/10.1093/bioinformatics/btp698


https://doi.org/10.1101/cshperspect.a021360



https://doi.org/10.1093/ve/vev003





Meyer M, Kircher M. 2010. Illumina sequencing library preparation for highly multiplexed target capture andsequencing. Cold Spring Harbor Protocols 2010:pdb.prot5448. DOI: https://doi.org/10.1101/pdb.prot5448,PMID: 20516186

Nicklisch N. 2017. Spurensuche an Skeletten. Palaodemografische und epidemiologische Untersuchungen anneolithischen und fru€hbronzezeitlichen Bestattungen aus dem Mittelelbe-Saale-Gebiet im Kontextpopulationsdynamischer Prozesse. In: Forschungsberichte des Landesmuseums fur Vorgeschichte Halle. 11

Patterson N, Price AL, Reich D. 2006. Population structure and eigenanalysis. PLoS Genetics 2:e190.DOI: https://doi.org/10.1371/journal.pgen.0020190, PMID: 17194218

Patterson Ross Z, Klunk J, Fornaciari G, Giuffra V, Duchene S, Duggan AT, Poinar D, Douglas MW, Eden JS,Holmes EC, Poinar HN. 2018. The paradox of HBV evolution as revealed from a 16th century mummy. PLoSPathogens 14:e1006750. DOI: https://doi.org/10.1371/journal.ppat.1006750, PMID: 29300782

Peltzer A, Jager G, Herbig A, Seitz A, Kniep C, Krause J, Nieselt K. 2016. EAGER: efficient ancient genomereconstruction. Genome Biology 17:60. DOI: https://doi.org/10.1186/s13059-016-0918-z, PMID: 27036623

Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. 2006. Principal components analysiscorrects for stratification in genome-wide association studies. Nature Genetics 38:904–909. DOI: https://doi.org/10.1038/ng1847, PMID: 16862161

Rambaut A, Lam TT, Max Carvalho L, Pybus OG. 2016. Exploring the temporal structure of heterochronoussequences using TempEst (formerly Path-O-Gen). Virus Evolution 2:vew007. DOI: https://doi.org/10.1093/ve/vew007

Rasche A, Souza B, Drexler JF. 2016. Bat hepadnaviruses and the origins of primate hepatitis B viruses. CurrentOpinion in Virology 16:86–94. DOI: https://doi.org/10.1016/j.coviro.2016.01.015, PMID: 26897577

Schaefer S. 2007. Hepatitis B virus taxonomy and hepatitis B virus genotypes. World Journal of Gastroenterology13:14–21. DOI: https://doi.org/10.3748/wjg.v13.i1.14, PMID: 17206751

Schuenemann VJ, Singh P, Mendum TA, Krause-Kyora B, Jager G, Bos KI, Herbig A, Economou C, Benjak A,Busso P, Nebel A, Boldsen JL, Kjellstrom A, Wu H, Stewart GR, Taylor GM, Bauer P, Lee OY, Wu HH, MinnikinDE, et al. 2013. Genome-wide comparison of medieval and modern Mycobacterium leprae. Science 341:179–183. DOI: https://doi.org/10.1126/science.1238286, PMID: 23765279

Simmonds P, Midgley S. 2005. Recombination in the genesis and evolution of hepatitis B virus genotypes.Journal of Virology 79:15467–15476. DOI: https://doi.org/10.1128/JVI.79.24.15467-15476.2005,PMID: 16306618

Skoglund P, Stora J, Gotherstrom A, Jakobsson M. 2013. Accurate sex identification of ancient human remainsusing DNA shotgun sequencing. Journal of Archaeological Science 40:4477–4482. DOI: https://doi.org/10.1016/j.jas.2013.07.004

Souza BF, Drexler JF, Lima RS, Rosario MO, Netto EM. 2014. Theories about evolutionary origins of humanhepatitis B virus in primates and humans. The Brazilian Journal of Infectious Diseases 18:535–543.

Thorvaldsdottir H, Robinson JT, Mesirov JP. 2013. Integrative genomics viewer (IGV): high-performancegenomics data visualization and exploration. Briefings in Bioinformatics 14:178–192. DOI: https://doi.org/10.1093/bib/bbs017, PMID: 22517427

Vagene AJ, Herbig A, Campana MG, Robles Garcıa NM, Warinner C, Sabin S, Spyrou MA, Andrades Valtuena A,Huson D, Tuross N, Bos KI, Krause J. 2018. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nature Ecology & Evolution 2:520–528. DOI: https://doi.org/10.1038/s41559-017-0446-6, PMID: 29335577

Veit U. 1996. Studien zum Problem der Siedlungsbestattungen im europaischen Neolithikum, Mu€nster. NewYork: Waxmann.

Warinner C, Herbig A, Mann A, Fellows Yates JA, Weiß CL, Burbano HA, Orlando L, Krause J. 2017. A robustframework for microbial archaeology. Annual Review of Genomics and Human Genetics 18:321–356.DOI: https://doi.org/10.1146/annurev-genom-091416-035526, PMID: 28460196

Warinner C, Rodrigues JF, Vyas R, Trachsel C, Shved N, Grossmann J, Radini A, Hancock Y, Tito RY, FiddymentS, Speller C, Hendy J, Charlton S, Luder HU, Salazar-Garcıa DC, Eppler E, Seiler R, Hansen LH, Castruita JA,Barkow-Oesterreicher S, et al. 2014. Pathogens and host immunity in the ancient human oral cavity. NatureGenetics 46:336–344. DOI: https://doi.org/10.1038/ng.2906, PMID: 24562188

WHO. 2017. Global Hepatitis Report.Zhou Y, Holmes EC. 2007. Bayesian estimates of the evolutionary rate and age of hepatitis B virus. Journal ofMolecular Evolution 65:197–205. DOI: https://doi.org/10.1007/s00239-007-0054-1, PMID: 17684696



https://doi.org/10.1101/pdb.prot5448


https://doi.org/10.1371/journal.pgen.0020190


https://doi.org/10.1371/journal.ppat.1006750


https://doi.org/10.1186/s13059-016-0918-z


https://doi.org/10.1038/ng1847

https://doi.org/10.1038/ng1847


https://doi.org/10.1093/ve/vew007

https://doi.org/10.1093/ve/vew007

https://doi.org/10.1016/j.coviro.2016.01.015


https://doi.org/10.3748/wjg.v13.i1.14




https://doi.org/10.1128/JVI.79.24.15467-15476.2005


https://doi.org/10.1016/j.jas.2013.07.004

https://doi.org/10.1016/j.jas.2013.07.004

https://doi.org/10.1093/bib/bbs017

https://doi.org/10.1093/bib/bbs017


https://doi.org/10.1038/s41559-017-0446-6

https://doi.org/10.1038/s41559-017-0446-6


https://doi.org/10.1146/annurev-genom-091416-035526


https://doi.org/10.1038/ng.2906


https://doi.org/10.1007/s00239-007-0054-1



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